Preference learning by matrix factorization on island models

Abstrakt

This paper presents island models, methods, implementation and experiments connecting stochastic optimization methods and recommendation task (collaborative one). Our models and methods are based on matrix factorization.

Parallel run of methods optimizes the RMSE metric from an island model point-of-view. This paper comments on architecture and some implementation decisions.

We dealt with two research hypotheses. First, whether island models bring always improvement.

We will show that almost always yes. Second, whether evolutionary algorithm does or does not always find the best solution.

This will be confirmed only on smaller data. Experiments were provided on Movie Lens 100k and 1M data.

Klíčová slova

• Island model
• stochastic optimization methods
• matrix factorization
• preference learning.